Load all required libraries.

library(tidyverse)
## Warning: package 'tidyverse' was built under R version 3.6.3
## -- Attaching packages ------------------------------------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.2     v purrr   0.3.4
## v tibble  3.0.3     v dplyr   1.0.0
## v tidyr   1.1.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.5.0
## Warning: package 'ggplot2' was built under R version 3.6.3
## Warning: package 'tibble' was built under R version 3.6.3
## Warning: package 'readr' was built under R version 3.6.3
## Warning: package 'dplyr' was built under R version 3.6.3
## Warning: package 'forcats' was built under R version 3.6.3
## -- Conflicts ---------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(plotly)
## Warning: package 'plotly' was built under R version 3.6.3
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(broom)
## Warning: package 'broom' was built under R version 3.6.3

Read in raw data from RDS.

raw_data <- readRDS("./n1_n2_cleaned_cases.rds")

Make a few small modifications to names and data for visualizations.

final_data <- raw_data %>% mutate(log_copy_per_L = log10(mean_copy_num_L)) %>%
  rename(Facility = wrf) %>%
  mutate(Facility = recode(Facility, 
                           "NO" = "WRF A",
                           "MI" = "WRF B",
                           "CC" = "WRF C"))

Seperate the data by gene target to ease layering in the final plot

#make three data layers
only_positives <<- subset(final_data, (!is.na(final_data$Facility)))
only_n1 <- subset(only_positives, target == "N1")
only_n2 <- subset(only_positives, target == "N2")
only_background <<-final_data %>% 
  select(c(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke, cases_per_100000_clarke)) %>%
  group_by(date) %>% summarise_if(is.numeric, mean)

#specify fun colors
background_color <- "#7570B3"
seven_day_ave_color <- "#E6AB02"
marker_colors <- c("N1" = '#1B9E77',"N2" ='#D95F02')
#remove facilty C for now
#only_n1 <- only_n1[!(only_n1$Facility == "WRF C"),]
#only_n2 <- only_n2[!(only_n2$Facility == "WRF C"),]

Build the main plot

      #first layer is the background epidemic curve
        p1 <- only_background %>%
              plotly::plot_ly() %>%
              plotly::add_trace(x = ~date, y = ~new_cases_clarke, 
                                type = "bar", 
                                hoverinfo = "text",
                                text = ~paste('</br> Date: ', date,
                                                     '</br> Daily Cases: ', new_cases_clarke),
                                alpha = 0.5,
                                name = "Daily Reported Cases",
                                color = background_color,
                                colors = background_color,
                                showlegend = FALSE) %>%
            layout(yaxis = list(title = "Clarke County Daily Cases", showline=TRUE)) %>%
            layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
        
        #renders the main plot layer two as seven day moving average
        p1 <- p1 %>% plotly::add_trace(x = ~date, y = ~X7_day_ave_clarke, 
                             type = "scatter",
                             mode = "lines",
                             hoverinfo = "text",
                            text = ~paste('</br> Date: ', date,
                                                     '</br> Seven-Day Moving Average: ', X7_day_ave_clarke),
                             name = "Seven Day Moving Average Athens",
                             line = list(color = seven_day_ave_color),
                             showlegend = FALSE)
      

        
        #renders the main plot layer three as positive target hits
        
        p2 <- plotly::plot_ly() %>%
          plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
                                       type = "scatter",
                                       mode = "markers",
                                       hoverinfo = "text",
                                       text = ~paste('</br> Date: ', date,
                                                     '</br> Facility: ', Facility,
                                                     '</br> Target: ', target,
                                                     '</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
                                       data = only_n1,
                                       symbol = ~Facility,
                                       marker = list(color = '#1B9E77', size = 8, opacity = 0.65),
                                       showlegend = FALSE) %>%
          plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
                                       type = "scatter",
                                       mode = "markers",
                                       hoverinfo = "text",
                                       text = ~paste('</br> Date: ', date,
                                                     '</br> Facility: ', Facility,
                                                     '</br> Target: ', target,
                                                     '</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
                                       data = only_n2,
                                       symbol = ~Facility,
                                       marker = list(color = '#D95F02', size = 8, opacity = 0.65),
                                       showlegend = FALSE) %>%
            layout(yaxis = list(title = "SARS CoV-2 Copies/L", 
                                 showline = TRUE,
                                 type = "log",
                                 dtick = 1,
                                 automargin = TRUE)) %>%
            layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
        
        #adds the limit of detection dashed line
        p2 <- p2 %>% plotly::add_segments(x = as.Date("2020-03-14"), 
                                          xend = ~max(date + 10), 
                                          y = 3571.429, yend = 3571.429,
                                          opacity = 0.35,
                                          line = list(color = "black", dash = "dash")) %>%
          layout(annotations = list(x = as.Date("2020-03-28"), y = 3.8, xref = "x", yref = "y", 
                                    text = "Limit of Detection", showarrow = FALSE))

        

        p1
## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## Warning: Ignoring 1 observations
        p2
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Combine the two main plot pieces as a subplot

p_combined <-
    plotly::subplot(p2,p1, # plots to combine, top to bottom
      nrows = 2,
      heights = c(.6,.4),  # relative heights of the two plots
      shareX = TRUE,  # plots will share an X axis
      titleY = TRUE
    ) %>%
    # create a vertical "spike line" to compare data across 2 plots
    plotly::layout(
      xaxis = list(
        spikethickness = 1,
        spikedash = "dot",
        spikecolor = "black",
        spikemode = "across+marker",
        spikesnap = "cursor"
      ),
      yaxis = list(spikethickness = 0)
    )
## Warning: Ignoring 1 observations
p_combined

Save the plot to pull into the index

save(p_combined, file = "./plotly_fig.rda")

Save an htmlwidget for website embedding

htmlwidgets::saveWidget(p_combined, "plotly_fig.html")

Build loess smoothing figures figures

#create smoothing data frames 
#n1
smooth_n1 <- only_n1 %>% select(-c(Facility)) %>% 
  group_by(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke, cases_per_100000_clarke) %>%
  summarize(sum_copy_num_L = sum(mean_total_copies)) %>%
  ungroup() %>%
  mutate(log_sum_copies_L = log10(sum_copy_num_L)) %>%
  mutate(target = "N1")
## `summarise()` regrouping output by 'date', 'cases_cum_clarke', 'new_cases_clarke', 'X7_day_ave_clarke' (override with `.groups` argument)
#n2
smooth_n2 <- only_n2 %>% select(-c(Facility)) %>% 
  group_by(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke, cases_per_100000_clarke) %>%
  summarize(sum_copy_num_L = sum(mean_total_copies)) %>%
  ungroup() %>%
  mutate(log_sum_copies_L = log10(sum_copy_num_L)) %>%
  mutate(target = "N2")
## `summarise()` regrouping output by 'date', 'cases_cum_clarke', 'new_cases_clarke', 'X7_day_ave_clarke' (override with `.groups` argument)
#add trendlines 
#extract data from geom_smooth
#n1 extract
# *********************************span 0.6***********************************
#*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_n1 <- ggplot(smooth_n1, aes(x = date, y = log_sum_copies_L)) + 
  stat_smooth(aes(outfit=fit_n1<<-..y..), method = "loess", color = '#1B9E77', 
              span = 0.6, n = 127)
## Warning: Ignoring unknown aesthetics: outfit
#n2 extract
extract_n2 <- ggplot(smooth_n2, aes(x = date, y = log_sum_copies_L)) + 
  stat_smooth(aes(outfit=fit_n2<<-..y..), method = "loess", color = '#1B9E77', 
              span = 0.6, n = 127)
## Warning: Ignoring unknown aesthetics: outfit
#look at the fits to align dates and total observations
#n1
extract_n1
## `geom_smooth()` using formula 'y ~ x'

fit_n1
##   [1] 10.90670 11.09491 11.27695 11.45247 11.62115 11.78263 11.93660 12.08270
##   [9] 12.22073 12.35103 12.47405 12.59025 12.70010 12.80405 12.90257 12.99416
##  [17] 13.07729 13.15236 13.21975 13.27983 13.33302 13.37968 13.42021 13.45499
##  [25] 13.48441 13.50887 13.52874 13.54441 13.55628 13.55454 13.53127 13.48968
##  [33] 13.43296 13.36433 13.28699 13.20413 13.11897 13.03469 12.95452 12.88164
##  [41] 12.81926 12.77059 12.73883 12.71176 12.67772 12.64073 12.60484 12.57407
##  [49] 12.55245 12.54400 12.54795 12.56028 12.58022 12.60698 12.63976 12.67780
##  [57] 12.72029 12.76646 12.81552 12.86669 12.91917 12.97219 13.02495 13.07668
##  [65] 13.13407 13.20291 13.28103 13.36623 13.45635 13.54921 13.64263 13.73444
##  [73] 13.82245 13.90449 13.97839 14.04195 14.09302 14.12941 14.15672 14.18162
##  [81] 14.20349 14.22170 14.23565 14.24472 14.24830 14.24239 14.22448 14.19618
##  [89] 14.15908 14.11480 14.06492 14.01106 13.95481 13.89778 13.84156 13.78777
##  [97] 13.73800 13.69385 13.65693 13.62264 13.58569 13.54666 13.50613 13.46467
## [105] 13.42284 13.38124 13.34043 13.30098 13.26348 13.22849 13.19660 13.16837
## [113] 13.14438 13.12334 13.10360 13.08522 13.06823 13.05269 13.03864 13.02612
## [121] 13.01520 13.00590 12.99828 12.99238 12.98825 12.98593 12.98548
#n2
extract_n2
## `geom_smooth()` using formula 'y ~ x'

fit_n2
##   [1] 10.71314 10.93445 11.14886 11.35612 11.55601 11.74830 11.93274 12.10912
##   [9] 12.27724 12.43730 12.58964 12.73457 12.87244 13.00357 13.12828 13.24573
##  [17] 13.35496 13.45621 13.54969 13.63561 13.71420 13.78566 13.85022 13.90810
##  [25] 13.95950 14.00465 14.04377 14.07707 14.10476 14.11805 14.10992 14.08327
##  [33] 14.04097 13.98593 13.92104 13.84919 13.77327 13.69617 13.62078 13.55000
##  [41] 13.48672 13.43383 13.39422 13.35296 13.29748 13.23420 13.16954 13.10992
##  [49] 13.06175 13.03145 13.01484 13.00329 12.99659 12.99452 12.99685 13.00335
##  [57] 13.01382 13.02803 13.04574 13.06675 13.09084 13.11777 13.14733 13.17929
##  [65] 13.22017 13.27499 13.34127 13.41655 13.49834 13.58419 13.67161 13.75814
##  [73] 13.84131 13.91864 13.98767 14.04593 14.09093 14.12022 14.13705 14.14723
##  [81] 14.15211 14.15303 14.15136 14.14843 14.14559 14.13874 14.12364 14.10144
##  [89] 14.07332 14.04043 14.00394 13.96500 13.92479 13.88445 13.84515 13.80805
##  [97] 13.77432 13.74512 13.72160 13.70031 13.67726 13.65279 13.62726 13.60104
## [105] 13.57447 13.54792 13.52174 13.49629 13.47193 13.44900 13.42788 13.40891
## [113] 13.39245 13.37775 13.36382 13.35069 13.33836 13.32685 13.31617 13.30634
## [121] 13.29738 13.28929 13.28210 13.27582 13.27046 13.26604 13.26257
#assign fits to a vector
n1_trend <- fit_n1
n2_trend <- fit_n2

#extract y min and max for each
limits_n1 <- ggplot_build(extract_n1)$data
## `geom_smooth()` using formula 'y ~ x'
limits_n1 <- as.data.frame(limits_n1)
n1_ymin <- limits_n1$ymin
n1_ymax <- limits_n1$ymax

limits_n2 <- ggplot_build(extract_n2)$data
## `geom_smooth()` using formula 'y ~ x'
limits_n2 <- as.data.frame(limits_n2)
n2_ymin <- limits_n2$ymin
n2_ymax <- limits_n2$ymax

#reassign dataframes (just to be safe)
work_n1 <- smooth_n1
work_n2 <- smooth_n2

#fill in missing dates to smooth fits
work_n1 <- work_n1 %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_n1 <- work_n1$date
work_n2 <- work_n2 %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_n2 <- work_n2$date

#create a new smooth dataframe to layer
smooth_frame_n1 <- data.frame(date_vec_n1, n1_trend, n1_ymin, n1_ymax)
smooth_frame_n2 <- data.frame(date_vec_n2, n2_trend, n2_ymin, n2_ymax)
#make plotlys

#plot smooth frames
p3 <- plotly::plot_ly() %>%
  plotly::add_lines(x = ~date_vec_n1, y = ~n1_trend,
                    data = smooth_frame_n1,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_n1,
                                  '</br> Median Log Copies: ', round(n1_trend, digits = 2),
                                  '</br> Target: N1'),
                    line = list(color = '#1B9E77', size = 8, opacity = 0.65),
                    showlegend = FALSE) %>%
plotly::add_lines(x = ~date_vec_n2, y = ~n2_trend,
                  data = smooth_frame_n2,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_n2,
                                  '</br> Median Log Copies: ', round(n2_trend, digits = 2),
                                  '</br> Target: N2'),
                    line = list(color = '#D95F02', size = 8, opacity = 0.65),
                    showlegend = FALSE) %>%
plotly::add_ribbons(x ~date_vec_n1, ymin = ~n1_ymin, ymax = ~n1_ymax,
                    showlegend = FALSE,
                    opacity = 0.25,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_n1, #leaving in case we want to change
                                  '</br> Max Log Copies: ', round(n1_ymax, digits = 2),
                                  '</br> Min Log Copies: ', round(n1_ymin, digits = 2),
                                  '</br> Target: N1'),
                    name = "",
                    line = list(color = '#1B9E77')) %>%
plotly::add_ribbons(x ~date_vec_n2, ymin = ~n2_ymin, ymax = ~n2_ymax,
                    showlegend = FALSE,
                    opacity = 0.25,
                    hoverinfo = "text",
                    text = ~paste('</br> Date: ', date_vec_n2, #leaving in case we want to change
                                  '</br> Max Log Copies: ', round(n2_ymax, digits = 2),
                                  '</br> Min Log Copies: ', round(n2_ymin, digits = 2),
                                  '</br> Target: N2'),
                    name = "",
                    line = list(color = '#D95F02')) %>%
                layout(yaxis = list(title = "Total Log SARS CoV-2 Copies", 
                                 showline = TRUE,
                                 automargin = TRUE)) %>%
                layout(xaxis = list(title = "Date")) %>%
    plotly::add_segments(x = as.Date("2020-06-24"), 
                                          xend = as.Date("2020-06-24"), 
                                          y = ~min(n1_ymin), yend = ~max(n1_ymax),
                                          opacity = 0.35,
                                          name = "Bars Repoen",
                                          hoverinfo = "text",
                                          text = "</br> Bars Reopen",
                                                 "</br> 2020-06-24",
                                          showlegend = FALSE,
                                          line = list(color = "black", dash = "dash")) %>%
    plotly::add_segments(x = as.Date("2020-07-09"), 
                                          xend = as.Date("2020-07-09"), 
                                          y = ~min(n1_ymin), yend = ~max(n1_ymax),
                                          opacity = 0.35,
                                          name = "Mask Mandate",
                                          hoverinfo = "text",
                                          text = "</br> Mask Mandate",
                                                 "</br> 2020-07-09",
                                          showlegend = FALSE,
                                          line = list(color = "black", dash = "dash")) %>%
    plotly::add_segments(x = as.Date("2020-08-20"), 
                                          xend = as.Date("2020-08-20"), 
                                          y = ~min(n1_ymin), yend = ~max(n1_ymax),
                                          opacity = 0.35,
                                          name = "</br> Classes Begin",
                                                 "</br> 2020-08-20",
                                          hoverinfo = "text",
                                          text = "Classes Begin",
                                          showlegend = FALSE,
                                          line = list(color = "black", dash = "dash")) %>%
      plotly::add_segments(x = as.Date("2020-10-03"), 
                                          xend = as.Date("2020-10-03"), 
                                          y = ~min(n1_ymin), yend = ~max(n1_ymax),
                                          opacity = 0.35,
                                          name = "</br> First Home Football Game",
                                                 "</br> 2020-10-03",
                                          hoverinfo = "text",
                                          text = "First Home Football Game",
                                          showlegend = FALSE,
                                          line = list(color = "black", dash = "dash")) %>%
  plotly::add_markers(x = ~date, y = ~log_sum_copies_L,
                      data = smooth_n1,
                       hoverinfo = "text",
                       showlegend = FALSE,
                       text = ~paste('</br> Date: ', date, 
                                     '</br> Actual Log Copies: ', round(log_sum_copies_L, digits = 2)),
                       marker = list(color = '#1B9E77', size = 6, opacity = 0.65)) %>%
    plotly::add_markers(x = ~date, y = ~log_sum_copies_L,
                      data = smooth_n2,
                       hoverinfo = "text",
                       showlegend = FALSE,
                       text = ~paste('</br> Date: ', date, 
                                     '</br> Actual Log Copies: ', round(log_sum_copies_L, digits = 2)),
                       marker = list(color = '#D95F02', size = 6, opacity = 0.65))

p3

Create final trend plot by stacking with epidemic curve

smooth_extract <-
    plotly::subplot(p3,p1, # plots to combine, top to bottom
      nrows = 2,
      heights = c(.6,.4),  # relative heights of the two plots
      shareX = TRUE,  # plots will share an X axis
      titleY = TRUE
    ) %>%
    # create a vertical "spike line" to compare data across 2 plots
    plotly::layout(
      xaxis = list(
        spikethickness = 1,
        spikedash = "dot",
        spikecolor = "black",
        spikemode = "across+marker",
        spikesnap = "cursor"
      ),
      yaxis = list(spikethickness = 0)
    )
## Warning: Ignoring 1 observations
smooth_extract
save(smooth_extract, file = "./smooth_extract.rda")